Sparse representation of point trajectories for action classification

Sivalingam, Ravishankar; Somasundaram, Guruprasad; Bhatawadekar, Vineet; Morellas, Vassilios; Papanikolopoulos, Nikolaos

doi:10.1109/icra.2012.6224777

Cited by 8 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tokyo, Japan 978-1-4673-6358-7/13/$31.00 ©2013 IEEE A large part of the action recognition methodology in this work most closely resembles the work of Sivalingam et al out of the same lab as this work [7]. When trying a NN algorithm and comparing results on segmented data from the Kinect and the accompanying Application Programming Interface (API), it was found that the trained classifiers produced satisfactory results without any additional, more complicated or computationally expensive algorithm.…”

Section: Ieee/rsj International Conference Onmentioning

confidence: 65%

Recognition of ballet micro-movements for use in choreography

Dancs

Sivalingam

Somasundaram

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

View full text Add to dashboard Cite

Computer vision as an entire field has a wide and diverse range of applications. The specific application for this project was in the realm of dance, notably ballet and choreography. This project was proof-of-concept for a choreography assistance tool used to recognize and record dance movements demonstrated by a choreographer. Keeping the commercial arena in mind, the Kinect from Microsoft was chosen as the imaging hardware, and a pilot set chosen to verify recognition feasibility. Before implementing a classifier, all training and test data was transformed to a more applicable representation scheme to only pass the important aspects to the classifier to distinguish moves for the pilot set. In addition, several classification algorithms using the Nearest Neighbor (NN) and Support Vector Machine (SVM) methods were tested and compared from a single dictionary as well as on several different subjects. The results were promising given the framework of the project, and several new expansions of this work are proposed.

show abstract

Section: Ieee/rsj International Conference Onmentioning

confidence: 65%

Recognition of ballet micro-movements for use in choreography

Dancs

Sivalingam

Somasundaram

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [24], advantages of both dense and sparse sampling are combined, and descriptors are extracted on a dense grid pruned either randomly or based on a sparse saliency mask of the underlying video. In [25], the authors compare two different representation schemes, raw multivariate time-series data and the covariance descriptors of the trajectories, and apply sparse representation techniques for classifying the various actions. The features are sparse coded using the orthogonal matching pursuit algorithm, and the gestures and actions are classified based on the reconstruction residuals.…”

Section: Previous Workmentioning

confidence: 99%

Dimensionality reduction of data sequences for human activity recognition

Chen

Wang

et al. 2016

Neurocomputing

View full text Add to dashboard Cite

“…109 Consequently, how to e®ectively represent the action trajectories is key. In their study, two di®erent representation schemes, one based on raw multivariate time-series data, and the other based on the covariance descriptors of the trajectories.…”

Section: Other Approachesmentioning

confidence: 99%

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Lun

Zhao

2015

Int. J. Patt. Recogn. Artif. Intell.

280

109

View full text Add to dashboard Cite

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classi¯cation of motion recognition techniques to highlight the di®erent approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lowerlevel computer vision tasks such as segmentation, object detection and human pose estimation.

show abstract

Sparse representation of point trajectories for action classification

Cited by 8 publications

References 29 publications

Recognition of ballet micro-movements for use in choreography

Recognition of ballet micro-movements for use in choreography

Dimensionality reduction of data sequences for human activity recognition

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Contact Info

Product

Resources

About